Neural networks have been used as a nonparametric method for option pricing and hedging
since the early 1990s. Far over a hundred papers have been published on this topic. This …

Reinforcement learning algorithms describe how an agent can learn an optimal action policy
in a sequential decision process, through repeated experience. In a given environment, the …

This chapter presents machine learning techniques and deep reinforcement learning-based
algorithms for the efficient resolution of nonlinear partial differential equations and dynamic …

Recent advances in machine learning are finding commercial applications across many
industries, not least the finance industry. This paper focuses on applications in one of the …

We propose a hybrid quantum-classical algorithm, which originated from quantum
chemistry, to price European and Asian options in the Black--Scholes model. Our approach …

Climate change poses a serious risk for insurance firms, threatening their sustainability from
numerous channels of impact. Assessing this impact, however, is not straightforward. We …

Given the competitiveness of a market-making environment, the ability to speedily quote
option prices consistent with an ever-changing market environment is essential. Thus, the …

This paper studies deep learning approaches to find optimal reinsurance and dividend
strategies for insurance companies. Due to the randomness of the financial ruin time to …

Current approaches to fair valuation in insurance often follow a two-step approach,
combining quadratic hedging with application of a risk measure on the residual liability, to …

Abstract Conditional Value-at-Risk (CVaR) is an extremely popular risk measure in finance
and is usually optimized to reduce the risk of large losses. This paper considers the CVaR …